Intercepting ADF Table Column Show/Hide Event with Custom Change Manager Class

Ever wondered how to intercept ADF table column show/hide event from ADF Panel Collection component? Yes, you could use ADF MDS functionality to store user preference for table visible columns. But what if you would want to implement it yourself without using MDS? Actually, this is possible through custom persistence manager class. I will show you how.

If you don't know what I'm talking about. Check below screenshot, this popup comes out of the box with ADF Panel Collection and it helps to manage table visible columns. Pretty much useful, especially for large tables:

Obviously, we would like to store user preference and next time the user comes back to the form, he should see previously stored setup for the table columns. One way to achieve this is to use out of the box ADF MDS functionality. But what if you don't want to use it? Still possible - we can catch all changes done through Manage Columns popup in custom Change Manager class. Extend from SessionChangeManager and override only a single method - addComponentChange. This is the place where we intercept changes and could log them to DB for example (later on form load, we could read table setup and apply it before fragment is rendered):

Register custom Change Manager class in web.xml:

Manage Columns popup is out of the box functionality offered by ADF Panel Collection component:

Method addComponentChange will be automatically invoked and you should see similar output when changing table columns visibility:

Download sample application code from my GitHub repository.

ADF Performance Improvement with Nginx Compression

We are using Nginx web server for Oracle ADF WorkBetter hosted demo hosted on DigitalOcean cloud server. Nginx helps to serve web application content fast and offer improved performance. One of the important tuning options - content compression, Nginx does this job well and is simple to setup.

Content compression doesn't provide direct runtime performance, a browser would run the same code, doesn't matter it was compressed or not. But it brings improved perceived performance (which is very important), network time is way faster, because of reduced content size. Oracle ADF is a server-side framework, each request would bring content from the server - faster this content comes, means better application performance.

1. Content Compression = OFF

Let see stats, when no content compression applied (using our Oracle ADF WorkBetter hosted demo).

Page load size is 2.69 MB transferred. Finish time 1.55 s:

Navigation to the employee section generates 165.76 KB and finish time 924 ms:

Navigation to employee compensation generates 46.19 KB and finish time 494 ms:

2. Nginx compression

Compression is simple to setup in Nginx. Gzip settings are set in nginx.conf, make sure to list all content types which must be supported for compression. Restart nginx process after new settings are saved in nginx.conf:

3. Content Compression = ON

Page load size is 733.84 KB transferred. Finish time 1.48 s:

Navigation to the employee section generates 72.75 KB and finish time 917 ms:

Navigation to employee compensation generates 7.59 KB and finish time 498 ms:

Jupyter Notebook — Forget CSV, fetch data from DB with Python

If you read a book, article or blog about Machine Learning — high chances it will use training data from CSV file. Nothing wrong with CSV, but let’s think if it is really practical. Wouldn’t be better to read data directly from the DB? Often you can’t feed business data directly into ML training, it needs pre-processing — changing categorial data, calculating new data features, etc. Data preparation/transformation step can be done quite easily with SQL while fetching original business data. Another advantage of reading data directly from DB — when data changes, it is easier to automate ML model re-train process.

In this post I describe how to call Oracle DB from Jupyter notebook Python code.

Step 1 

Install cx_Oracle Python module:

python -m pip install cx_Oracle

This module helps to connect to Oracle DB from Python.

Step 2

cx_Oracle enables to execute SQL call from Python code. But to be able to call remote DB from Python script, we need to install and configure Oracle Instant Client on the machine where Python runs.

If you are using Ubuntu, install alien:

sudo apt-get update 
sudo apt-get install alien 

Download RPM files for Oracle Instant Client and install with alien:

alien -i oracle-instantclient18.3-basiclite-18.3.0.0.0–1.x86_64.rpm 
alien -i oracle-instantclient18.3-sqlplus-18.3.0.0.0–1.x86_64.rpm 
alien -i oracle-instantclient18.3-devel-18.3.0.0.0–1.x86_64.rpm 

Add environment variables:

export ORACLE_HOME=/usr/lib/oracle/18.3/client64 
export PATH=$PATH:$ORACLE_HOME/bin 

Read more here.

Step 3 

Install Magic SQL Python modules:

pip install jupyter-sql 
pip install ipython-sql 

Installation and configuration complete.

For today sample I’m using Pima Indians Diabetes Database. CSV data can be downloaded from here. I uploaded CSV data into the database table and will be fetching it through SQL directly in Jupyter notebook.

First of all, the connection is established to the DB and then SQL query is executed. Query result set is stored in a variable called result. Do you see %%sql — this magic SQL:


Username and password must be specified while establishing a connection. To avoid sharing a password, make sure to read password value from the external source (it could be simple JSON file as in this example or more advanced encoded token from keyring).

The beauty of this approach — data fetched through SQL query is out of the box available in Data Frame. Machine Learning engineer can work with the data in the same way as it would be loaded through CSV:

Sample Jupyter notebook available on GitHub. Sample credentials JSON file.

JDeveloper 12c IDE Performance Boost

There is a way to optimize JDeveloper 12c IDE performance by disabling some of the features you are not using.

I was positively surprised with improved JDeveloper responsiveness after turning off some of the features. ADF BC, Task Flow, and ADF Faces wizards started to respond in a noticeably faster way. Simple change and big performance gain, awesome.

One of the strongest JDeveloper performance improvements come from disabling TopLink feature. Ironically - TopLink is an abandoned product (12.1.3 was the last release). I remember back in 2006 TopLink was very promising and it was almost becoming the default platform for ADF Model. One of the old blog posts written by me related to TopLink - External Transaction Service in Oracle TopLink. But luckily it was overshadowed by ADF BC.

These are the features I disabled in my JDeveloper to get performance gain:

Cross Field Form Validation in Oracle JET

JET keeps evolving and in the latest versions  - toolkit provides improved support for form cross-field validation. It is much easier to implement validation than it was before. I will show it in this example.

Example of the data entry form. Validation logic:

- Invoice Date before Payment Due Date and Payment Date
- Payment Due Date before Payment Date

Example when two fields fail validation:

JET provides component called validation group. Form can be wrapped by this component to identify if any validation errors are reported there. For example, when calling JS function, before proceeding with the function code - we can check if validation group contains errors:

Input field can be assigned with custom validator function:

Example of validation function code where cross-field validation logic is implemented - we compare field value with other fields. If validation rule condition is false - validation error is thrown:

Example of function code, where validation group is checked for errors. If there are errors in the current validation group - errors are displayed and the first field with error is focused:

Download sample code from my GitHub repo.

Search Form in Oracle Visual Builder based on ADF BC REST

Oracle Visual Builder supports ADF BC REST out of the box. Build service connection using "Define by Specification" wizard:

Wizards support ADF as API type. Add describe at the end of the REST URL, this will bring metadata for exposed ADF BC REST service (information about attribute types, etc.):

List of endpoints will be populated automatically. You could select all endpoints to be supported for your connection or select only few:

The most typical thing you would do with endpoint - map it with the table to display collection data. You would drag and drop Oracle JET table into VBCS page and choose Add Data option to map it with the service connection:

In the wizard you would select previously defined service connection:

There is a way to switch wizard to detailed view and choose from multiple endpoints available for the connection:

In the next step, you would select service attributes to be displayed in table columns. All declarative, sweet:

In Visual Builder at any point you can quickly test application, it will load in separate browser tab (or you could switch app to Live mode and test page functionality directly in VBCS window):

Every action in Visual Builder is handled through events. For example, this event is mapped with Reset button (you can see it in structure tab on the left):

At any point, you can switch to source view and check (or edit) HTML/JET code which is generated for you by Visual Builder. So cool, imagine typing and copy-pasting all this text by hand, tough and time-consuming (you could do better things in your life than copy-pasting HTML code):

Let's explain how search form logic is done in this sample. I have defined page scope variable type, this type would hold search attribute name, type and operation:

Create as many variables based on this type, as many search criteria items you will expect to have. Make sure to provide attribute and operation names (leave value property empty, this will be assigned by user):

Map search form fields with variables:

Create an event for Search button, which calls search action chain:

In action chain we can define search logic. Before executing search criteria, we need to prepare search criteria array (normally this step could be skipped, but there is issue in current Visual Builder, it fails to execute criteria search, when at least one of the criteria items empty). Calling custom JavaScript function where search criteria array will be prepared:

Custom JavaScript function, it helps to prepare array to be based to criteria (if search item is not set, we are assigning empty value):

Result of the function is mapped with service connection criteria, search will be executed automatically:

Table pagination is handled automatically too. Make sure to specify scroll policy = loadMoreOnScroll and define fetch size:

Resources:

1. Sample source code on my GitHub
2. Blog from Shay - Filtering Data Providers with Compound Conditions in Visual Builder
3. Blog from Shay - Oracle JET UI on Top of Oracle ADF With Visual Builder
4. My previous post about query logic in Visual Builder - Query Logic Implementation in VBCS for ADF BC REST

Announcing Hosting for Oracle ADF Rich Client and Oracle ADF WorkBetter Demos

If you are curious about how Oracle ADF works or want to explore a rich set of ADF Faces components - welcome to access Oracle ADF demo apps hosted on our cloud server.

We launched a dedicated website Oracle ADF Components. Hosted demos:

1. ADF Faces Rich Client
2. ADF Work Better

These demo apps can be downloaded from Oracle, you could run them on your own environment too. But sometimes it is useful to have apps online for quick access.